code: def test_quantize_openvino_option(self):
      model = mobilenet_v3_small(num_classes=10)
  
      x = torch.rand((10, 3, 256, 256))
      y = torch.ones((10,), dtype=torch.long)
  
      ds = TensorDataset(x, y)
      dataloader = DataLoader(ds, batch_size=2)
  
      optimized_model = InferenceOptimizer.quantize(
          model,
          accelerator="openvino",
          calib_data=dataloader,
          openvino_config={"PERFORMANCE_HINT": "LATENCY"},
      )
  
      optimized_model(x[0:1])
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 3 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_quantize_openvino_option() without return types